Evaluation of a multimodal diagnostic algorithm for prediction of cognitive impairment in elderly patients with dizziness.

Abstract

The current diagnostic workup for chronic dizziness in elderly patients often neglects neuropsychological assessment, thus missing a relevant proportion of patients, who perceive dizziness as a subjective chief complaint of a concomitant cognitive impairment. This study aimed to establish risk prediction models for cognitive impairment in chronic dizzy patients based on data sources routinely collected in a dizziness center. One hundred patients (age: 74.7 7.1years, 41.0% women) with chronic dizziness were prospectively characterized by (1) neuro-otological testing, (2) quantitative gait assessment, (3) graduation of focal brain atrophy and white matter lesion load, and (4) cognitive screening (MoCA). A linear regression model was trained to predict patients' total MoCA score based on 16 clinical features derived from demographics, vestibular testing, gait analysis, and imaging scales. Additionally, we trained a binary logistic regression model on the same data sources to identify those patients with a cognitive impairment (i.e., MoCA < 25). The linear regression model explained almost half of the variance of patients' total MoCA score (R2 = 0.49; mean absolute error: 1.7). The most important risk-predictors of cognitive impairment were age (β = -0.75), pathological Romberg's sign (β = -1.05), normal caloric test results (β = -0.8), slower timed-up-and-go test (β = -0.67), frontal (β = -0.6) and temporal (β = -0.54) brain atrophy. The binary classification yielded an area under the curve of 0.84 (95% CI 0.70-0.98) in distinguishing between cognitively normal and impaired patients. The need for cognitive testing in patients with chronic dizziness can be efficiently approximated by available data sources from routine diagnostic workup in a dizziness center.